import numpy as np


########################################################################
#                         Download information                         #
########################################################################

FORM_URL = "http://www.scan-net.org/ScanNet"


########################################################################
#                              Data splits                             #
########################################################################

SCANS = {
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        'scans/scene0488_00',
        'scans/scene0488_01',
        'scans/scene0490_00',
        'scans/scene0494_00',
        'scans/scene0496_00',
        'scans/scene0500_00',
        'scans/scene0500_01',
        'scans/scene0518_00',
        'scans/scene0527_00',
        'scans/scene0535_00',
        'scans/scene0549_00',
        'scans/scene0549_01',
        'scans/scene0550_00',
        'scans/scene0552_00',
        'scans/scene0552_01',
        'scans/scene0553_00',
        'scans/scene0553_01',
        'scans/scene0553_02',
        'scans/scene0558_00',
        'scans/scene0558_01',
        'scans/scene0558_02',
        'scans/scene0559_00',
        'scans/scene0559_01',
        'scans/scene0559_02',
        'scans/scene0565_00',
        'scans/scene0568_00',
        'scans/scene0568_01',
        'scans/scene0568_02',
        'scans/scene0574_00',
        'scans/scene0574_01',
        'scans/scene0574_02',
        'scans/scene0575_00',
        'scans/scene0575_01',
        'scans/scene0575_02',
        'scans/scene0578_00',
        'scans/scene0578_01',
        'scans/scene0578_02',
        'scans/scene0580_00',
        'scans/scene0580_01',
        'scans/scene0583_00',
        'scans/scene0583_01',
        'scans/scene0583_02',
        'scans/scene0591_00',
        'scans/scene0591_01',
        'scans/scene0591_02',
        'scans/scene0593_00',
        'scans/scene0593_01',
        'scans/scene0595_00',
        'scans/scene0598_00',
        'scans/scene0598_01',
        'scans/scene0598_02',
        'scans/scene0599_00',
        'scans/scene0599_01',
        'scans/scene0599_02',
        'scans/scene0606_00',
        'scans/scene0606_01',
        'scans/scene0606_02',
        'scans/scene0607_00',
        'scans/scene0607_01',
        'scans/scene0608_00',
        'scans/scene0608_01',
        'scans/scene0608_02',
        'scans/scene0609_00',
        'scans/scene0609_01',
        'scans/scene0609_02',
        'scans/scene0609_03',
        'scans/scene0616_00',
        'scans/scene0616_01',
        'scans/scene0618_00',
        'scans/scene0621_00',
        'scans/scene0629_00',
        'scans/scene0629_01',
        'scans/scene0629_02',
        'scans/scene0633_00',
        'scans/scene0633_01',
        'scans/scene0643_00',
        'scans/scene0644_00',
        'scans/scene0645_00',
        'scans/scene0645_01',
        'scans/scene0645_02',
        'scans/scene0647_00',
        'scans/scene0647_01',
        'scans/scene0648_00',
        'scans/scene0648_01',
        'scans/scene0651_00',
        'scans/scene0651_01',
        'scans/scene0651_02',
        'scans/scene0652_00',
        'scans/scene0653_00',
        'scans/scene0653_01',
        'scans/scene0655_00',
        'scans/scene0655_01',
        'scans/scene0655_02',
        'scans/scene0658_00',
        'scans/scene0660_00',
        'scans/scene0663_00',
        'scans/scene0663_01',
        'scans/scene0663_02',
        'scans/scene0664_00',
        'scans/scene0664_01',
        'scans/scene0664_02',
        'scans/scene0665_00',
        'scans/scene0665_01',
        'scans/scene0670_00',
        'scans/scene0670_01',
        'scans/scene0671_00',
        'scans/scene0671_01',
        'scans/scene0678_00',
        'scans/scene0678_01',
        'scans/scene0678_02',
        'scans/scene0684_00',
        'scans/scene0684_01',
        'scans/scene0685_00',
        'scans/scene0685_01',
        'scans/scene0685_02',
        'scans/scene0686_00',
        'scans/scene0686_01',
        'scans/scene0686_02',
        'scans/scene0689_00',
        'scans/scene0690_00',
        'scans/scene0690_01',
        'scans/scene0693_00',
        'scans/scene0693_01',
        'scans/scene0693_02',
        'scans/scene0695_00',
        'scans/scene0695_01',
        'scans/scene0695_02',
        'scans/scene0695_03',
        'scans/scene0696_00',
        'scans/scene0696_01',
        'scans/scene0696_02',
        'scans/scene0697_00',
        'scans/scene0697_01',
        'scans/scene0697_02',
        'scans/scene0697_03',
        'scans/scene0699_00',
        'scans/scene0700_00',
        'scans/scene0700_01',
        'scans/scene0700_02',
        'scans/scene0701_00',
        'scans/scene0701_01',
        'scans/scene0701_02',
        'scans/scene0702_00',
        'scans/scene0702_01',
        'scans/scene0702_02',
        'scans/scene0704_00',
        'scans/scene0704_01'],

    'test': [
        'scans_test/scene0707_00',
        'scans_test/scene0708_00',
        'scans_test/scene0709_00',
        'scans_test/scene0710_00',
        'scans_test/scene0711_00',
        'scans_test/scene0712_00',
        'scans_test/scene0713_00',
        'scans_test/scene0714_00',
        'scans_test/scene0715_00',
        'scans_test/scene0716_00',
        'scans_test/scene0717_00',
        'scans_test/scene0718_00',
        'scans_test/scene0719_00',
        'scans_test/scene0720_00',
        'scans_test/scene0721_00',
        'scans_test/scene0722_00',
        'scans_test/scene0723_00',
        'scans_test/scene0724_00',
        'scans_test/scene0725_00',
        'scans_test/scene0726_00',
        'scans_test/scene0727_00',
        'scans_test/scene0728_00',
        'scans_test/scene0729_00',
        'scans_test/scene0730_00',
        'scans_test/scene0731_00',
        'scans_test/scene0732_00',
        'scans_test/scene0733_00',
        'scans_test/scene0734_00',
        'scans_test/scene0735_00',
        'scans_test/scene0736_00',
        'scans_test/scene0737_00',
        'scans_test/scene0738_00',
        'scans_test/scene0739_00',
        'scans_test/scene0740_00',
        'scans_test/scene0741_00',
        'scans_test/scene0742_00',
        'scans_test/scene0743_00',
        'scans_test/scene0744_00',
        'scans_test/scene0745_00',
        'scans_test/scene0746_00',
        'scans_test/scene0747_00',
        'scans_test/scene0748_00',
        'scans_test/scene0749_00',
        'scans_test/scene0750_00',
        'scans_test/scene0751_00',
        'scans_test/scene0752_00',
        'scans_test/scene0753_00',
        'scans_test/scene0754_00',
        'scans_test/scene0755_00',
        'scans_test/scene0756_00',
        'scans_test/scene0757_00',
        'scans_test/scene0758_00',
        'scans_test/scene0759_00',
        'scans_test/scene0760_00',
        'scans_test/scene0761_00',
        'scans_test/scene0762_00',
        'scans_test/scene0763_00',
        'scans_test/scene0764_00',
        'scans_test/scene0765_00',
        'scans_test/scene0766_00',
        'scans_test/scene0767_00',
        'scans_test/scene0768_00',
        'scans_test/scene0769_00',
        'scans_test/scene0770_00',
        'scans_test/scene0771_00',
        'scans_test/scene0772_00',
        'scans_test/scene0773_00',
        'scans_test/scene0774_00',
        'scans_test/scene0775_00',
        'scans_test/scene0776_00',
        'scans_test/scene0777_00',
        'scans_test/scene0778_00',
        'scans_test/scene0779_00',
        'scans_test/scene0780_00',
        'scans_test/scene0781_00',
        'scans_test/scene0782_00',
        'scans_test/scene0783_00',
        'scans_test/scene0784_00',
        'scans_test/scene0785_00',
        'scans_test/scene0786_00',
        'scans_test/scene0787_00',
        'scans_test/scene0788_00',
        'scans_test/scene0789_00',
        'scans_test/scene0790_00',
        'scans_test/scene0791_00',
        'scans_test/scene0792_00',
        'scans_test/scene0793_00',
        'scans_test/scene0794_00',
        'scans_test/scene0795_00',
        'scans_test/scene0796_00',
        'scans_test/scene0797_00',
        'scans_test/scene0798_00',
        'scans_test/scene0799_00',
        'scans_test/scene0800_00',
        'scans_test/scene0801_00',
        'scans_test/scene0802_00',
        'scans_test/scene0803_00',
        'scans_test/scene0804_00',
        'scans_test/scene0805_00',
        'scans_test/scene0806_00']}


########################################################################
#                                Labels                                #
########################################################################

NYU40_NUM_CLASSES = 40

NYU40_IDS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39]

SCANNET_NUM_CLASSES = len(NYU40_IDS)

NYU40_2_SCANNET = np.repeat(SCANNET_NUM_CLASSES, NYU40_NUM_CLASSES + 1)
NYU40_2_SCANNET[NYU40_IDS] = np.arange(len(NYU40_IDS))

NYU40_STUFF_IDS = [1, 2]

CLASS_COLORS = np.asarray([
    [174.0, 199.0, 232.0],   # wall
    [152.0, 223.0, 138.0],   # floor
    [ 31.0, 119.0, 180.0],   # cabinet
    [255.0, 187.0, 120.0],   # bed
    [188.0, 189.0,  34.0],   # chair
    [140.0,  86.0,  75.0],   # sofa
    [255.0, 152.0, 150.0],   # table
    [214.0,  39.0,  40.0],   # door
    [197.0, 176.0, 213.0],   # window
    [148.0, 103.0, 189.0],   # bookshelf
    [196.0, 156.0, 148.0],   # picture
    [ 23.0, 190.0, 207.0],   # counter
    [247.0, 182.0, 210.0],   # desk
    [219.0, 219.0, 141.0],   # curtain
    [255.0, 127.0,  14.0],   # refrigerator
    [158.0, 218.0, 229.0],   # shower
    [ 44.0, 160.0,  44.0],   # toilet
    [112.0, 128.0, 144.0],   # sink
    [227.0, 119.0, 194.0],   # bathtub
    [ 82.0,  84.0, 163.0],   # otherfurniture
    [  0.0,   0.0,   0.0]])  # ignored

CLASS_NAMES = [
    "wall",
    "floor",
    "cabinet",
    "bed",
    "chair",
    "sofa",
    "table",
    "door",
    "window",
    "bookshelf",
    "picture",
    "counter",
    "desk",
    "curtain",
    "refrigerator",
    "shower curtain",
    "toilet",
    "sink",
    "bathtub",
    "otherfurniture",
    "ignored"]

# For instance segmentation
MIN_OBJECT_SIZE = 100
STUFF_CLASSES = [i for i, idx in enumerate(NYU40_IDS) if idx in NYU40_STUFF_IDS]
THING_CLASSES = [i for i in range(SCANNET_NUM_CLASSES) if not i in STUFF_CLASSES]
